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Taxonomy and Categorization of Uncertainties in Space
Systems with an Application to the Measurement of the
Value of Adaptability
Alejandro Salado1, Roshanak Nilchiani
2 and Mahmoud Efatmaneshnik
3
Stevens Institute of Technology, Hoboken, NJ, 07030
Space systems face multiple types of uncertainties from the design phase through
production, testing, launch, operation and retirement of the space system that challenge the
mission success in multiple dimensions and aspects. Therefore proper identification,
classification, categorization and management of uncertainties are necessary in
understanding the environment that space systems are embedded and also essential in
identifying the adaptable designs, architectures, or solutions. Given the ever increasing
dynamic environment of current space systems, sources of uncertainties are considerably
diverse and therefore make proper identification and management a crucial part of design
and operation of adaptable and Flexible Space Systems. This paper aims on a thorough and
holistic taxonomy and categorization of space systems uncertainties for the purpose of
keeping track of uncertainties and facilitate their prioritization, management, scenario
building and appropriate modeling during the entire life cycle for the purpose of designing
Adaptable and Flexible Space Systems. Several major types of uncertainties were organized
into five major groups including policy, service performance, organization, technology and
market; which are derived from the stakeholders and mapping the space system context.
The taxonomy has been defined ensuring completeness and coherency. Then various
classification types based on uncertainty dimension, being exogenous or endogenous, level of
complexity and other classification types are presented. This research also addresses the
peculiarities of the space systems according to their type of mission and customer.
I. Introduction
NCERTAINTY is the hallmark of all complex systems. It is the lack of complete knowledge of the state of a
particular system in the present or in the future. Adaptability, flexibility and other systems -illities are devoid
of meaning in a hypothetical deterministic world. Real world exists in the context of uncertainties of various kinds
that dramatically affect all natural and man-made systems. The concept of uncertainty has been discussed and
studied in various domains of knowledge including Physics, Statistics, Economics, Finance, Insurance, Engineering,
Information Sciences, Philosophy, Psychology, Sociology and many more.
A thorough study of the concept of uncertainty in space systems as well as other domains of knowledge indicates
that a detailed differentiation between various types of uncertainties is required and a comprehensive classification is
lacking. Space systems, like any other natural or man-made system, behave and respond differently when facing
various types of uncertainty. Therefore a comprehensive classification is a must. The next step, which involves using
various modeling techniques to model uncertainty, also needs the classification step in order to know what type
modeling is most appropriate.
1 Doctoral student, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030. AIAA
Member. 2 Assistant Professor, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030. AIAA
Associate Member. 3 Assistant Research Professor, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030.
AIAA Member.
U
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Furthermore current prospect in design techniques for systems level consider the treatment of uncertainty as a
leverage to assess the value of different candidate solutions1-7
. Consequently a common taxonomy that provides a
comprehensive yet usable set of uncertainties is required so that a high level of confidence can be achieve in such
trade-offs.
This paper is organized as follows. Section II provides a review of existing literature on the topic of uncertainties
in space systems, with emphasis on their management at system level, and synthesizes their limitations. Section III
presents several classifications that can be used to understand uncertainties from different perspectives. The
taxonomy proposed in this paper is presented in section IV and is validated against assessed limitations in section V.
The paper finishes giving a short summary of the conclusions and a proposal for future work in section VI.
II. Literature Review
Uncertainty research has traditionally focused on uncertainty and risk reduction8,9
and uncertainty modeling8.
Such approach has resulted in classification of uncertainties based on the physics behind the uncertainty, as for
example the classification described by Thunnissen and Tsuyuki9:
Recent trends look at uncertainties as opportunity/threat elements, trying to incorporate uncertainties into the
evaluation of system level trade-offs so that systems are designed to cope with uncertainties rather than simply
limiting the designers to achieve their mere reduction1-7
. Under this new scope the use of the classification presented
here before despite being useful for modeling of uncertainties is not convenient to perform trade-offs. McManus and
Hastings10
propose to classify uncertainties from designer perspective and conclude that the right approach is to set
the knowledge base as the measurable element. In this way they identify five types of uncertainties, namely: lack of
knowledge, lack of definition, statistically characterized phenomena, known unknowns, and unknown unknowns.
These are further described in the next section.
In the new scenario of evaluating the effect of uncertainties at the system level has brought in interest in non-
technical uncertainties, which had traditionally been ignored, such as political uncertainty, development uncertainty,
requirement stability uncertainty, or market uncertainty7,11-13
. A review of existing literature shows that there is no
consensus on which uncertainties need to be considered for system level trade-offs. For example, Hastings et al.
make a selection of 10 uncertainties7 while Yao et al. use a combination of those uncertainties as well as several
technical ones11
. Furthermore, their selection follows the knowledge and expertise of the engineers at hand. Several
limitations of these approaches are listed in Table 1.
Uncertainty
Ambiguity Epistemic Aleatory Interaction
Model Phenomenological Behavioral
Approximation errors
Numerical errors
Programming errors
Design
Requirement
Volitional
Human errors
Figure 1. Thunnissen and Tsuyuki classification of uncertainties9
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An interesting element of the collection proposed by Hastings et al
7 is the differentiated collection of
uncertainties according to the life-cycle phase, which implies that the success of a system does not only depend on
how it performs, but that it begins at the very beginning of the design phase.
III. Classifications of uncertainties
Uncertainties can be classified differently depending on the aspect of the uncertainty that is of interest. In this
section, several types of classification are presented. Some exist in literature of the various domains of knowledge,
and some we have created the classifications such as Dimensions, and Nature of uncertainty. Figure 2 shows a brief
set of our various types of classifications, which will be discussed next.
A. Dimensions
Dimension classification addresses how the uncertainty occurs in temporal and physical dimensions.
Uncertainties in space systems can be categorized based in three dimensions: the timeframe and the lifecycle phase
in which they manifest themselves, the level of physical detail and abstraction in which they appear, and the type of
Table 1. Main limitations of existing collections of uncertainties.
Limitation Justification
Lack of
completeness
Uncertainties are identified without following a structured process. Instead
brainstorming or field knowledge are used. Therefore it is not possible to ensure that the
selected uncertainties are the right ones (validation of the selected uncertainties) or their
completeness.
Lack of
weighting
Different uncertainties have different effects on the system and therefore the
criticality of their impacts shall be reflected on the trade-offs. Although it can be argued
that weighting is part of the design process and not of the framework, the high
complexity of decisions in space systems due to the amount of decision takers involved,
it is recommended to provide a “default” weighting system embedded on the framework.
Lack of
distinction based
on mission type
Similar justification with regards weighting, since impact of uncertainties may be
perceived differently depending on the type of mission.
It can also be the case that some uncertainties exist for some missions and not for
others (particularly when moving down in the architectural hierarchy).
Lack of
distinction based
on customer
Similar justification with regards weighting, since impact of uncertainties may be
perceived differently depending on the customer.
It can also be the case that some uncertainties exist for some customers and not for
others (particularly when moving down in the architectural hierarchy).
Limited to space
segment
Segments others that the space segment are not considered in the categorizations.
However, they may have a major contribution to the end value for money of space
systems, particularly during operations.
Mix of different
abstraction levels
For example considering high level uncertainties like cost or market and low level
ones like semi-major axis or inclination11
. Makes it difficult to measure and use
uncertainties at the right level of abstraction. Threatens the completeness of the
uncertainties taken into account.
Lack of
organizational
uncertainties
Project related organizational aspects can affect the successful completion of a space
system, e.g. leave of key personnel, adequate project organization, unsuitable supplier
selection, etc. are not addressed.
Uncertainty
Classification
Dimensions Objective vs.
Subjective
Endogenous vs.
Exogenous
Roots and
Sources
Nature of
uncertainty
Figure 2. Classifications of uncertainties
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change (object of variation) that creates the uncertainty. For example, Launch Failure is a valid uncertainty only
within the timeframe of launch to successful insertion of the space system, whereas orbital debris uncertainty affects
a space system once it is launched and deployed and until the end of its operation.
B. Objective Vs. Subjective
Objective vs. Subjective classification addresses the way in which the uncertainty is estimated.
Objective uncertainties are those subjected to rules that remain relatively constant over time (in relation to the
lifetime of the space system), often follow a statistical probabilistic distribution of an uncertain physical
phenomenon, and therefore can be studied and estimated with a high degree of confidence. They include
epistemological and ontological based uncertainties such as subsystems reliability, space debris, or availability,
which often follow the probabilistic laws of physics.
Subjective uncertainties are those for which the rules may change dramatically and therefore high confidence
levels cannot be reached when estimating them through in-depth studies. Some examples of this type of
uncertainties include market uncertainty, project schedule uncertainty, and technological development uncertainty.
C. Endogenous Vs. Exogenous
Another form of categorization is based on the boundaries of the system under study. Based on the boundaries
drawn, uncertainties can be exogenous to the space system, such as market uncertainties and probability of collision
with space debris, or endogenous, such as part, subsystem, and instrument failure on board of the spacecraft.
D. Roots and Sources
McManus and Hastings10
propose five categories to classify uncertainties according to their roots, namely:
Lack of knowledge, which includes facts that are not known, or are known only imprecisely.
Gathering this type of knowledge can reduce the uncertainty.
Lack of definition, which exists when the elements or attributes of a system are not specified.
Statistically characterized phenomena, which represent elements that cannot always be known
precisely, but that can be statistically characterized or bounded.
Figure 3. Dimensions classification of uncertainties
Figure 4. Endogenous vs. Exogenous uncertainty classification description
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Known unknowns, which refer to those uncertainties that are identified, but that cannot be reduced
beforehand.
Unknown unknowns, which refer to emergent behaviors of a system, i.e. there is no awareness of
their existence until they actually occur.
These roots have different levels and depth of uncertainty. For example, the uncertainty associated with the lack
of knowledge and definition is often much less than the uncertainty associated with unknown unknowns. Figure 4.6
conceptually shows the depth of uncertainty in these five defined categories of uncertainty.
E. Uncertainty nature
This type of categorization of the uncertainty is based on the rules and dynamics inherent to the nature of
uncertainty under study. We define three major categories: Simple, Complicated, and Complex.
The Simple category consists of the types that follow the single cause and effect. A complicated uncertainty
follows a series of cause and effects and in a way is a superimposition of some simple processes. These types of
uncertainties are often easier to model in a statistical manner. Examples of complicated/Simple Uncertainties include
reliability of subsystem or the space system, orbital debris, or availability. If they are modeled well, an agent that is
interfering with the problem at hand to address the effect of uncertainty can resolve and respond to the uncertainty.
In contrast, Complex uncertainties are often governed by very different internal dynamics. An adaptable
response implemented by an agent to reduce the complex uncertainty, can change the uncertainty profile and
magnitude, or even initiate emergence of a new type of uncertainty. Complex uncertainties are often governed with
circular causality and feedback loops. Circular causality in essence is a sequence of causes and effects whereby the
explanation of a pattern leads back to the first cause and either confirms or changes that first cause. They can show
chaotic behavior, a small change in the cause implies dramatic effects or unforeseen types of problems. Complex
uncertainties are also associated with emergence, unpredictability and entropy. Figure 5 shows a conceptual figure
of the Complex uncertainty concept.
Complex uncertainties are understudied and less
understood in various domains of engineering. A
void of research on modeling and understanding
these types of uncertainties exists. The current
existing literature puts the emphasis on reduction of
complexity in its modeling, and many use expert
judgment or opinion in characterizing these types
of uncertainties. Examples of complex uncertainties
include technological obsolescence uncertainty,
technological development uncertainty, schedule
uncertainty, policy and economic uncertainties, and
many more. The complex uncertainties show the following behavior in nature:
Dynamic: The complex uncertainty profile changes in time and is dynamic. For example, an action taken
by an agent to reduce the funding uncertainty can start a chain of events with feedbacks in the system,
which can have an adverse effect in resolving the uncertainty.
Governed by feedback: There are often several feedback loops and nested loops with reinforcing or
balancing nature in the system.
Nonlinear: An interference in the uncertainty doesn’t have a linear correlation to the magnitude of
interference
Adaptive and evolving: complex uncertainties adapt themselves to the solutions that the agent provides
them. They can show resistance to the solution provided by the agent to resolve the uncertainty.
Time lag: there is often a time lag between the action of the agent and behavior of the complex
uncertainty. The time lag creates the illusion of dissociation between the action of the agent and the
complex uncertainty.
Counterintuitive: Cause and effect are distant in time and space. High leverage points are often not
obvious.
Policy Resistance: “Common sense” solutions often don’t work; many obvious solutions fail or actually
worsen the problem.
Figure 5. Schematics of the concept of Complex
Uncertainty.
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IV. Development of a taxonomy for uncertainties
Taxonomy for system trade-off has been developed to cope with the limitations presented in section II, ensuring
completeness of the uncertainties and applicability of the taxonomy to any space system. The taxonomy is
independent of the classifications presented in the previous section. It is not intended to classify types of
uncertainties, but rather to provide a structure in which the uncertainties space systems face can be rationally
organized.
The taxonomy is composed of 27 uncertainties organized in 5 groups and addressed at a space system level, thus
it includes the space and launch segments, the ground segment, and the mission segment. The selection of
uncertainty categories has been done bearing always in mind the ultimate use of the taxonomy, which is assessing
value of a solution within a trade-off among different space system alternatives.
A. Application to the design of adaptable and flexible systems
As presented in the introductory section system attributes like adaptability or flexibility are only meaningful in
the context of uncertainty. In fact, the need to produce adaptable and flexible systems stems from coping with
uncertainty. Consequently, the better the uncertainties are modeled, estimated and ultimately understood, the better
the value of different candidate systems can be assessed.
Due to the ever-increasing complexity of space systems and the amount of interaction they have with other
systems, isolated modeling of discipline uncertainties is not sufficient for evaluating candidate systems. Instead the
design team should acquire a complete understanding of the influence a particular uncertainty has on the overall
system. This can be facilitated by gathering together the following elements:
Taxonomy that structures uncertainties from a system point of view and that ensures completeness of the
uncertainty identification process.
Description of positive and negative consequences of the uncertain event to the system.
Mathematical description of the uncertainty (modeling technique, conveyed information, and model).
Interdependencies between the different uncertainties, understood as those that are modified after an
uncertain event occurs (complex uncertainties).
Importance of a particular uncertainty within the range of uncertainties for the particular system of
interest. For space systems this is mainly driven by the type of mission and the type of customer.
B. Taxonomy
The idea behind providing taxonomy to organize uncertainties is ensuring completeness and providing a
common reference under which different systems can be evaluated. In order to achieve this goal the taxonomy is
structured following a sequential conceptualization of the elements that affect the end value of a space system.
These elements respond to five questions that incrementally challenge the value of the system:
1) Is it allowed to build and use the system?, which addresses the Legality of the system of interest.
Uncertainties responding to this question are labeled “Policy uncertainties”.
2) Is it feasible to build and use the system?, which addresses the feasibility of having the system. Due to the
nature of space system uncertainties responding to this question are technological and therefore have been
labeled as “Technology uncertainties”.
3) Can we build and operate the system?, which addresses the actual capability of industry and government to
implement the system and make it usable. Uncertainties responding to this question are labeled “Capability
uncertainties”.
4) Does the system operates within the initial specified performance level?, which addresses the level of
functionality and performance the system offers once the system is operational with respect to its intended levels.
Uncertainties responding to this question are labeled “Service performance uncertainties”.
5) Is the system successful?, which addresses the actual use or recognition of the system once it is operational.
Uncertainties responding to this question are labeled “Market uncertainties”.
The proposed categories are hierarchical with respect to value provided by the system in the sense that the upper
levels are enabled by the lower ones. This enabling mechanism shall not be understood as sequential in time,
because uncertainties in each category may affect the system at different phases of the system life cycle.
Table 2 presents the proposed taxonomy and provides additional description of each of its categories.
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Because the selection of categories for uncertainties cover all aspects of how the value of a system may be
affected, virtually any specific uncertainty a space system would face could be mapped to one of the categories.
Therefore the taxonomy provides the means for structurally identifying uncertainties in a system and thus guarantees
that every aspect related to its value is evaluated. Consequently the taxonomy increases the confidence in the
evaluation of all uncertain events that ultimately may affect the value of a system.
C. Types of missions and customers
Space systems are well compartmentalized, i.e. there are conceptually few and well-known uses for space
systems. The characteristics, challenges, and objectives of each use are generally shared between the majority of
space systems for a particular use, resulting in similar concept of operations. These uses are traditionally recognized
as types of space missions. Furthermore the economics of the space market make those characteristics, challenges,
and objectives differ according to the purpose of each specific mission. Therefore uses of space systems can also be
further determined given the type of customer acquiring the space system.
In the proposed categorization uncertainties are weighted according to the type of mission and customer for
which the space system is developed. This differentiation in importance for the uncertainties increases the quality of
the analysis when determining value of adaptable and flexible systems, when several uncertainties are treated
simultaneously. It shall be noted that although the table in Appendix provides specific figures for the particular
weights these are only informative and the determination of the actual value for a specific space system is left open
to each project so that the values of the actual customer can be properly captured.
Table 3 lists the types of missions that have been considered as part of the present research. They are the result
of analyzing the concepts of operations and mission objectives of existing space systems and grouping those that are
similar in approach and purpose.
Table 2. Description of the taxonomy categories
Category Description
Policy
uncertainties
Uncertainties related to law and regulation that impact the system. Most common
examples include ITAR, EO laws, or ITU frequency allocation.
This type of uncertainty has not really been explored in the available literature. When
discussing Policy uncertainty, it is normally related to government funding, which in this
taxonomy is allocate to market.
Technology
uncertainties
Uncertainties that are related to the availability of technology or technical solutions.
Most common examples are obsolescence, state-of-the-art, achievability, Technology
Readiness Levels (TRL’s), System Readiness Levels (SRL), etc.
Capability
uncertainties
Uncertainties that are related to the capability of the project team and may impact the
development or the operation of the system. Most common examples include supply
chain performance, complexity of operations, directives to use specific suppliers, loss of
key personnel, inadequate personnel, etc.
Uncertainties falling under this category have not been addressed by existing
literature, despite the fact they are usually the major contributor to cost overruns, schedule
delays, and technical non-compliances in space systems.
Service
performance
uncertainties
Uncertainties that are related to the impacts of bringing the system into real-life
operation. They could be defined also as uncertainties included in the design by definition
(performance based on probabilities). Most common examples may include reliabilities,
availabilities, transmitted power, degradation, lifetime, orbit accuracy, fuel usage,
radiation, atmospheric effects, network load, integration to other systems, etc.
Market
uncertainties
Uncertainties related to “funding and revenues”, which may be impacted by business
case success or effects of internal and external competitors:
Commercial project: market capture, effect of other company putting the
system in place earlier or at lower cost, impact of competitors with same
service in other industry (e.g. terrestrial networks).
Government project: actual scientific return, competitors making funding
fluctuate (e.g. budget moved from Human spaceflight to Earth observation),
etc.
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Table 4 lists the types of customers that have been considered as part of the present research. They reflect the
typical customers existing in the space market, which are mainly defined by the system or customer objectives and,
more importantly, by the funding source.
D. Identification of uncertainties
Uncertainties may differ from project to project and it is virtually impossible to list all uncertainties a system will
face during its development and lifetime. However, the proposed taxonomy identifies 27 uncertainties that are
considered common to each and every space system and in addition, are considered sufficiently complete to
holistically evaluate candidate solutions at system level.
Identification of uncertainties has been done using a hybrid approach:
Top-down: in addition to evaluating the uncertainties inherent to the system itself, typical stakeholders for
space systems are also consulted. Such a process aims at providing completeness because all elements
affecting the success (value) of the space system are considered.
Down-top: uncertainties listed in existing literature, as presented in section II, have been gathered and
organized according to their applicability in terms of system decomposition. This step serves as
validation for the top-down approach.
Because of their nature uncertainties are described as positive and negative consequences for the system. These
are defined for different moments in time of the system life cycle. For simplicity under the scope of the present
research two phases are considered: development phase and operational phase (including launch, LEOP and IOT).
Table 5 lists the identified uncertainties with their correspondent stakeholder and provides a brief description of their
meaning. Table 6 provides a description of positive and negative consequences of the uncertainties when the
triggering event occurs during development phase and table 7 does the same for the operational phase.
Table 3. Mission types
Type of mission Description / Justification
Communications Provides any type of communication link using space assets, being them inter-
satellite or satellite-to-ground.
Navigation Provides positioning and navigation services to ground or space users. It shall be
noted that although its concept of operations falls under the ‘Communications’
paradigm, a dedicated category has been implemented because the actual constraints
may be different depending on the customer. For example, a degradation of the
availability or service reliability of a government owned satellite used for technology
development can easily not affect the mission objectives. However, those same
requirements for a navigation constellation must be fulfilled, since other systems may
rely upon them for their performance, including safety of people lives (for example
landing an airplane).
Earth Observation Provides information about the Earth using space assets.
Science Pursues the advancement of science beyond its boundaries using space assets. This
category includes the so-called ‘Exploration missions’ because both share similar
constraints and objectives, being usually the mission context identical.
Human Spaceflight Although Human ‘Spaceflight’ is nowadays directly linked to science, the fact of
humans being part of the system makes it peculiar enough to form a stand-alone type.
Table 4. Customer types
Type of customer Description / Justification
Commercial Projects funded by private companies, normally aiming at profiting from a service
provided using space assets.
Government Projects funded by public fund, i.e. eventually the tax payer, normally aiming at
stretching the boundaries of science or develop new technologies that could be used
by the private sector.
Military Projects funded by public funds, i.e. eventually the tax payer, normally aiming at
improving the military capacity of a country.
Specific characteristics of military projects such as for example handling of
classified information are sufficient to make it a stand-alone type of customer.
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The proposed taxonomy does not include the uncertainty of budget or funding fluctuations, despite it is usually
considered in existing literature. This decision has been taken by analyzing why budget may fluctuate (increase or
decrease) for a space system. When thinking in those terms budget fluctuation can be seen as a consequence of a
combination of other factors such as competition or policy. For example, a change in policy of how space must be
explored may result in shifting budget from human spaceflight to science. This effect could be seen as an increase in
competition. Consequently the taxonomy only accounts for the factors that are considered root causes, thus leaving
budget fluctuation out.
Table 5. Description of uncertainties.
Uncertainty Stakeholder Description
Policy
Export Law and regulations Export restrictions (e.g. ITAR).
Frequency
allocation Law and regulations Loss/availability of frequency channels to operate.
Mission-specific
regulations Law and regulations
Mission-specific regulations affecting operational concept and
implementation.
Disposal Law and regulations
Regulations on how to deal with the space assets once
operations are finished (e.g. debris management).
Technology
Obsolescence Technology Obsolescences.
Technology
readiness Technology Technology development risks (e.g. TRL).
System readiness
Technology/Manufac
turer Integration of technologies (e.g. SRL).
Capability
Supply chain Manufacturers/Policy Supply chain performance.
Cost
Manufacturers /
Customer Validity of the development and operational costs estimations.
Technical
capability Manufacturers Technical capability to develop the system.
Key people Manufacturers Dismissal of key people.
V&V Manufacturer Validity of the verification and validation program.
Design Manufacturer
Correctness of the subsystem designs towards system
objectives.
Requirements Customer Fluctuation of requirements.
Customer
involvement Customer Level and type of involvement of the customer.
Service performance
Reliability Customer System reliability (e.g launcher, component, ground, etc.)
Availability Customer Service availability.
Debris Space environment Probability of orbital debris colliding with the space segment.
Radiation Space environment Space radiation damages the space segment.
Weather hazard Earth climate Earthquakes and alike (Ground segment).
Lifetime Space environment
Lifetime of the system, normally limited by degradation of the
space segment.
Performance
Technology /
Manufacturers
Actual performance of the space system (e.g. receive power,
contact window, etc.).
Market
Market size User Size of the market targeted by the system.
Discount rate Customer Uncertainty of the prediction of the discount rate.
Competitor Competitor
Competition affecting the success of the project. It may have
different definitions dpending on the type of customer.
Market capture User Actual part of the market using the system.
Schedule Manufacturers Time to market.
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Table 6. Description of positive/negative consequences during development phase.
Uncertainty Positive consequence Negative consequence
Policy
Export
Access lower cost or better location launcher; use
of better componenets (improve performance or
reduce cost).
Limitation on the use of launcher leading to
redesign or requalification; multiple launcher
selection leading to overdesign; inability to use
specific components leading to overruns and
redesign.
Frequency
allocation Use a higher bandwidth.
Redesign to accommodate newer frequency;
improve filtering; narrow bandwidth.
Mission-
specific
regulations Open to increase intended service. Limiting intended service.
Disposal None. Redesign to fulfil debris regulations.
Technology
Obsolesc.
Use of newer technology that improves
performance.
Cost and schedule impact to find a replacement.
The later the obsolescence triggers, the worse the
imapct for the project.
Technology
readiness Performance better than targeted.
Performance not achieved; cost overruns; schedule
delays.
System
readiness Integration of technologies
Performance not achieved; cost overruns; schedule
delays.
Capability
Supply chain
Having some elements earlier may allow for de-
risking activities.
Having some elements earlier may increase costs
due to storage.
Late deliveries have a negative impact on
schedule.
Cost Cost to develop the system is lower than expected.
Cost to develop the system is higher than
expected.
Technical
capability
Team performs better than expected and system is
developed with fewer errors thus leading to lower
costs; or achieve better performance than required.
Team does not have the capability to develop the
system and thus system does not achieve
performance; or it takes longer than required to
achieve it.
Key people
Some key people act as bottlenecks. Although at
short term it may seem as negative to the project,
substitution may lead to faster processes (removal
of bottleneck).
Difficult replacement leads to incapibility to
achieve performance (develop system).
V&V N/A N/A
Design
Smooth integration at system level, reducing
schedule/cost.
Difficult integration at system level with cost
overruns and schedule dealys.
Reqs.
Less stringent requirements leading to lower cost
and fastest time to market. Change requiring redesign of the system.
Customer
involvement
Customer supportive: allows manufacturer to do
the job and provides pragmatic expertise to add
value to the development.
Customer limitting: forces bouracracy without
added value and limits the manufacturer from
carrying ist job.
Service performance
Uncertainties related to service performance are not applicable during development phase because their trigerring event
can only occur once the system is operational (note: real performance).
Market
Market size N/A N/A
Discount rate N/A N/A
Competitor
Less competition than expected may lead to higher
funding.
More competition than expected may lead to
lower funding (government) and/or change in
requirements.
Market
capture N/A N/A
Schedule
Time to develop the system is shorter than
expected.
Time to develop the system is longer than
expected.
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Table 7. Description of positive/negative consequences during operational phase.
Uncertainty Positive consequence Negative consequence
Policy
Export N/A N/A
Frequency
allocation Use of higher bandwidth. Interference/jamming from another satellite.
Mission-
specific
regulations Open to increase intended service. Limiting intended service.
Disposal None.
Reduction in lifetime to accommodate disposal
needs.
Technology
Obsolescence
Improved performance; reduction of operational
costs in the long run. Upgrade costs; potential temporal loss of service.
Technology
readiness N/A N/A
System
readiness N/A N/A
Capability
Supply chain N/A N/A
Cost Profit is higher than expected. Profit is lower than expected.
Technical
capability N/A N/A
Key people N/A N/A
V&V
System survives/performes under potential non-
nominal situations.
System does not perform or survive under nominal
or almost nominal situations with respective
reduction in service provided.
Design N/A N/A
Requirements
New needs fulfilled by the system (potential
higher market capture or maintain it).
New needs not fulfilled by the system (potential
reduction in market capture).
Customer
involvement N/A N/A
Service performance
Reliability
Reduction on recovery actions leading to
reduction of operational costs.
Service degradation (even potential loss of
service); increase on operational costs for
increased recovery actions.
Availability Higher revenues. Service degradation leading to a loss in revenue.
Debris None.
Service degradation (even potential loss of
service); increase on operational costs for
increased recovery actions.
Radiation None.
Service degradation (even potential loss of
service); increase on operational costs for
increased recovery actions.
Weather
hazard None.
Service degradation (even potential loss of
service); increase on operational costs for
increased recovery actions.
Lifetime
Increase on the depreciation time of assets
(revenues at USD 0 cost on assets).
Service degradation (even potential loss of
service).
Performance Better performance than expected. Worse performance than expected.
Market
Market size
A market higher than expected may lead to higher
reveneus.
A market lower than expected may lead to lower
reveneus than expected.
Discount rate Higher profit. Lower profit.
Competitor
Less competition may lead to higher revenues or
funding.
More competition may lead to lower revenues or
funding.
Market
capture
More users than expected that may lead tl higher
revenues.
Less users than expected that may lead to lower
revenues.
Schedule N/A N/A
American Institute of Aeronautics and Astronautics
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The identified uncertainties within the proposed taxonomy are applicable to any type of mission and customer,
yet market uncertainties need some discussion in order to understand their meaning outside the commercial
customer. Table 8 provides a description of the meaning each uncertainty belonging to Market category has to
different customers. Such definitions have been developed by exploring the uncertainties from a conceptual
perspective and not from their literal meaning, thinking about Market as the community of potential users of the
system, being these consumers in the commercial environment or scientists in the government one, for example.
As presented earlier uncertainties need to be prioritized according to stakeholder’s demands. For space systems
these are related to the mission type and the customer type. A prioritization matrix is proposed as part of the
taxonomy so that proper evaluation of candidate solutions can be performed. The matrix, presented in table 9, shall
be considered only for illustrative purposes since no evidence for actual figures has been used, besides the rationale
and experience of the authors.
Table 8. Definition of Market uncertainties for each type of customer
Market
uncertainty
Customer type
Commercial Government Military
Market size Size of the market
addressable by the system.
Scientific community
that could use the system.
Population that could
benefit from the system.
Total amount of military
conflicts.
Discount
rate
Opportunity cost of
capital.
Opportunity cost of
scientific or social revenue.
Opportunity cost of
upgraded military capacity.
Competitor New competitors
entering the market while
the system is being
developed or when
operational.
Other projects or market
segment getting public /
government interest that
make budget / funding
fluctuate (e.g. budget
moving from Earth
observation to Human
Spaceflight).
Other governmental
agencies. Governments may
transfer funding between
the different agencies
making the budget/funding
fluctuate.
Market
capture
Actual part of the
market using the system.
Actual part of the
scientific community using
what the system delivers.
Actual usage by
populations/agencies/etc. of
what the system delivers.
Amount of conflicts
(percentage) where the
system can be used.
Schedule Time to market. Time to bring the
system into operation.
Time to bring the
system into operation.
American Institute of Aeronautics and Astronautics
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E. Uncertainty Interdependencies
As presented in section III, uncertainties of complex nature are those in which a change by an agent to resolve
and address the uncertainty at hand, potentially changing its probability distribution and trigger events, results in
emergence of new uncertainties. In space industry, we can observe many examples of complex uncertainties and
how they create a ripple effect in the larger system of systems level due to the interdependencies between specific
uncertainty types. In order to evaluate the impact of complex uncertainties on the value of a system, it is necessary
to understand how the different space systems uncertainties are related. The authors believe that correlation finding
and determining interdependencies between various types of space systems is a critical domain of research. The
initial studies of interdependencies between the space systems uncertainties of the proposed taxonomy by the
Table 9. Prioritization of uncertainties.
Uncertainty (N) Communications Navigation
Earth
Observation Science
Human
Spaceflight
C G M G M C G M G C G
Policy
Export 3 2 1 2 1 1 2 1 1 1 1
Frequency
allocation 2 2 N 2 N 3 3 N 3 3 3
Mission-specific
regulations 3 3 N 3 N 1 2 N 3 1 1
Disposal 3 3 N 2 N 2 2 N 3 3 3
Technology
Obsolescence 1 1 1 2 2 3 3 3 3 2 2
Technology
readiness N 1 1 1 1 2 1 1 1 1 1
System readiness N 1 1 1 1 2 1 1 1 1 1
Capability
Supply chain 1 2 2 2 2 1 2 2 2 1 2
Cost 1 2 2 2 2 1 2 2 2 1 2
Technical
capability 2 2 1 2 1 2 2 1 1 2 2
Key people 3 2 1 2 1 3 2 1 1 2 2
V&V 2 1 1 2 1 1 2 1 2 1 2
Design 1 2 1 2 1 1 2 1 2 1 2
Requirements 1 2 1 2 2 1 2 1 2 1 2
Customer
involvement 3 2 2 2 2 3 2 2 2 3 2
Service performance
Reliability 1 2 1 1 1 2 3 1 2 1 1
Availability 1 3 1 2 1 1 2 1 3 3 3
Debris 1 2 1 2 1 2 3 1 3 1 1
Radiation 1 2 1 2 1 2 3 1 3 1 1
Weather hazard 3 3 2 2 2 1 2 3 3 3 3
Lifetime 1 3 2 3 3 1 2 3 2 1 2
Performance 2 1 1 2 1 2 1 1 1 2 1
Market
Market size 1 2 2 2 2 1 2 2 2 1 2
Discount rate 2 3 3 3 3 2 3 3 3 2 3
Competitor 2 3 3 3 3 2 3 3 3 2 3
Market capture 1 3 3 3 3 1 3 3 3 1 3
Schedule 1 2 2 3 2 1 2 1 3 2 3
Legend: N – Not applicable; 1 – High priority; 2 – Medium priority; 3 – Low priority.
American Institute of Aeronautics and Astronautics
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authors are presented in a matrix form in Figure 6. Note that dependencies are one-directional: relation from A
(cause) to B (consequence) does not imply a relation exist from B (as a cause) to A (as a consequence).
Justification and clarification for such dependencies are provided in the appendix. We believe that an in depth
research on interdependencies of uncertainties, can guide us to be more informed about the probable consequence of
unfolding uncertainties and guide us to understand how an specific uncertainty can trigger other types. The
importance of this research also due to its application in calculating the value of Adaptability and Flexibility: The
scenarios that are building according to the interdependencies of space systems create a clearer picture of the options
and their sequence, and therefore, a more precise valuation of options in or on the space systems.
V. Validation: overcoming existing limitations
The proposed taxonomy has been developed aiming at mitigating the limitations that are present in existing
literature and that have been discussed in section II. Table 10 summarizes which elements of the taxonomy mitigate
such limitations.
Ob
sole
scen
ce
Tech
no
log
y r
ead
iness
Sy
stem
read
iness
Reli
ab
ilit
y
Av
ail
ab
ilit
y
Deb
ris
Rad
iati
on
Weath
er
hazard
Lif
eti
me
Perf
orm
an
ce
Mark
et
size
Dis
co
un
t ra
te
Co
mp
eti
tor
Mark
et
cap
ture
Sch
ed
ule
Su
pp
ly c
hain
Co
st
Tech
nic
al
cap
ab
ilit
y
Key
peo
ple
V&
V
Desi
gn
Req
uir
em
en
ts
Cu
sto
mer
inv
olv
em
en
t
Ex
po
rt
Fre
qu
en
cy
all
ocati
on
Mis
sio
n-s
pecif
ic r
eg
ula
tio
ns
Dis
po
sal
Obsolescence 11 12 21 41 100 110 79
Technology readiness 1 13 72 101 111 80
System readiness 2 73 102 112 81
Reliability 22 42
Availability 63 68 113
Debris 23 43 99
Radiation 24 44
Weather hazard 25 45
Lifetime 3 18 26 31 34 38 ? ? ?
Performance ? 60 64 69
Market size 27 46 52 65 135 82 92
Discount rate
Competitor 61 70 103 123 136 83 93
Market capture 28 47 53 66 84 94
Schedule 4 6 14 32 35 62 67 71 104 114 124 137 146 85
Supply chain 74 115 150 127 147
Cost 139 148
Technical capability 7 15 54 75 128 132 140 149
Key people 119
V&V 19 29 48 56
Design 20 30 49 57 129
Requirements 9 17 36 37 39 ? 58 77 116 120 130 133 96
Customer involvement 78 105 117 121 125 131 134 141
Export 5 10 40 55 107 106 118 122 126 142
Frequency allocation ? 108 143
Mission-specific
regulations ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 144 ? 88
Disposal 33 ? ? ? 145
Market
Capability
Policy
Capability Legal
Tech.
Service perf.
Columns are triggered by rows
Techn. Service performance Market
Figure 6. Uncertainties dependencies.
American Institute of Aeronautics and Astronautics
15
VI. Conclusion
The present paper begins with a review of classification of uncertainties in existing literature for space systems.
In particular, several classifications have been gathered that look at uncertainties from different perspectives, namely
dimension, objective vs. subjective, endogenous vs. exogenous, roots and causes, and nature. The authors also
contribute to new types of classifications of uncertainty in space systems.
A taxonomy that groups and identifies a complete set of uncertainties that are of use for the value assessment of
adaptable and flexible space systems has been discussed and developed by authors. The taxonomy is characterized
by two major features:
Structures the identification process of uncertainties, ensuring completeness and coherency.
Provides the necessary information package to use uncertainties in multi-disciplinary trade-offs at system
level for space systems.
The present discussion has shown that the proposed taxonomy mitigates the limitations when dealing with
uncertainties in approaches described in existing literature. We also have presented some initial results of our studies
in finding correlations and interdependencies between various space systems related uncertainties which are crucial
in building realistic scenarios that can be used for valuation of flexibility and adaptability in space systems.
Finally the present research supports proposes advances and future work on the following domains:
Collect evidence to provide validated figures on priorities of uncertainties.
Develop models for capability and policy uncertainties, which have not been explored so far in existing
literature, yet they are a major contributor in development success of space systems.
Explore the nature and dynamics of complex uncertainties.
More in depth studies of the uncertainty dependencies presented in this paper, correlation finding through
studies of more historical cases.
Appendix
Table 11 lists the impacts uncertainties may have on the different segments of a space systems and which new
uncertainties might be triggered as a consequence to a change in the originating uncertainty. Note the following
legend: U – Utility; C – Cost; S – Schedule; S – Space Segment; G – Ground Segment; and M – Mission Segment.
Table 10. Mitigation of limitations in existing literature.
Limitation Mitigation in proposed taxonomy
Lack of completeness Structured taxonomy with conceptualization process of 5 steps. Use of
stakeholders and confidence review with existing literature (section IV.B and
section IV.D).
Lack of weighting The taxonomy includes prioritization of uncertainties based on customer type
and mission type (section IV.D).
Lack of distinction
based on mission type
The taxonomy includes prioritization of uncertainties based on customer type
and mission type (section IV.C).
Lack of distinction
based on customer
The taxonomy includes prioritization of uncertainties based on customer type
and mission type (section IV.C).
Limited to space
segment
Uncertainties have been identified analyzing a space system at the system
level, i.e. considering the traditional segments (section IV.B, section IV.D and
appendix).
Mix of different
abstraction levels
Structured taxonomy at system level. Hybrid approach to determine
uncertainties at the appropriate level (section IV.B and IV.D).
Lack of
organizational /
capability
uncertainties
Specific category in the taxonomy (section IV.B).
American Institute of Aeronautics and Astronautics
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Table 12 provides justification and clarification for the uncertainty interdependencies listed in section IV.E.
Table 11. Effects of uncertainty on segments and triggering of additional uncertainties
Uncertainty (N)
Development phase Operational phase
Effect Segment Uncertainties
triggered
Effect Segment Uncertainties
triggered (group) U C S S G M U C S S G M
Policy
Export X X X X
Requirements/Supply
chain N/A
Frequency allocation X X X X X X Requirements X X Market capture
Mission-specific
regulations X X X X Market capture X X X X Market capture
Disposal X X X Requirements X X N/A
Techonlogy
Obsolescence X X X X N/A X X X X N/A
Technology
readiness X X X X N/A N/A
System readiness X X X X N/A Capability
Capability
Schedule X X X X N/A X N/A
Supply chain X X X X X Schedule X X X X X X Schedule
Cost X X X X X Schedule X X X N/A
Technical capability X X X X X X Performance/Schedule Performance
Key people X X X X X X Technical capability Performance
V&V X X X X X N/A
Design X X X X X
Schedule/Technical
capability
Requirements X X X X X Technology X X X X X Market capture
Customer
involvement X X X X X X
Schedule/Technical
capability
Service performance
Reliability N/A X X X X X N/A
Availability N/A X X X Market capture
Debris N/A X X X N/A
Radiation N/A X X X N/A
Weather hazard N/A X X X X N/A
Lifetime N/A X X N/A
Performance N/A X X X X Market capture
Market
Market size N/A X Availability
Discount rate N/A X N/A
Competitor N/A X X X Marlet capture
Market capture N/A X Availability
American Institute of Aeronautics and Astronautics
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Table 12. Justification for uncertainty interdependencies.
ID Example ID Example
1
The later a technology may be available the higher the risk
of obsolescence for other parts of the system. Same in opposite direction. 76 N/A
2
The later a system may be available the higher the risk of
obsolescence for some parts of the system. Same in
opposite direction. 77
Change in requirement may lead to redesign, which may affect
schedule.
3
The higher the lifetime the higher the risk of obsolescence.
Same in opposite direction. 78
The level of customer involvement may lower or speed the
development process.
4 The longer it takes to put the service in operation the higher the risk of obsolescence. Same in opposite direction. 79
Risk of obsolescence may drive governments to limit their
exports of that product, or of the new substitutors in order to have technological advantage.
5
The higher the limitation in export regulation for key
technologies the higher the probability to use old technologies and therefore the higher the risk of
obsolescence. Same in opposite direction. 80
Export regulations may be affected by availability of new
technology in order to have technological advantage.
6
An increase on the time to market allows for more time to
develop new technologies and therefore the uncertainty of technology readiness may be lower. 81 Same rationale as for 80.
7
If the technical capability is not sufficient, the uncertainty
to achieve a specific level of technology readiness may be higher. 82
Goevrnments may want to limit a specific service in a region in order to limit their capabilities.
8 N/A 83 Same rationale as for 82.
9
Requirements may change toward more stringent
performance needs, which would result in new TRL
uncertainties. 84 Same rationale as for 82.
10
Export regulations may suddenly forbid the export of a specific technology and therefore its TRL would change
for that project. 85
The longer it takes to put the system into service the higher the
probability of law changes.
11
Obsolescence of a technology may change a TRL from 1 to 7, i.e. from available technology to new development.
Therefore their uncertainties are related. 86 N/A
12
Obsolescence of a technology or component may make a SRL change from available to not available. Therefore
their uncertainties are related. 87 N/A
13
The later a technology may be available the later a system
may be ready. 88
Mission specific regulations may limit use of foreigner
technologies.
14
An increase on the time to market allows for more time to
integrate new technologies and therefore the uncertainty of
system readiness may be lower. 89 N/A
15 Same rationale as for 7. 90 N/A
16 N/A 91 N/A
17 Same rationale as for 9. 92 The higher the market size, the higher the bandwidth and maybe there are not enough in the selected frequency channel.
18
The longer the service has to operate the lower its
reliability may be. 93
A competitor entering earlier in the marketplace may take the
frequency slot.
19
A wrong verification approach may result in early failures not identified and therefore the reliability uncertainty
might be affected. 94 Same rationale as for 93.
20 A wrong design may result in an actual lower reliability. 95 N/A
21
Obsolescences when in operation may result in longer
times to repair or upgrade and therefore availability may be
affected. 96
Change in requirements may lead to different frequency use,
which then may not be available.
22
The system availability dependes on the system reliability
and therefore their uncertainties as well. 97 N/A
23 The more actual debris in space the higher the risk of collapsing (lower availability) and the opposite. 98 N/A
24 Same rationale as for 23, but with radiation. 99
The higher the debris content, the higher the probability agencies
force to perform disposal operations.
25 Same rationale as for 23, but with weather hazard. 100 The higher the obsolescence, the riskier the supply chain.
American Institute of Aeronautics and Astronautics
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Table 12 cont. Justification for uncertainty interdependencies.
ID Example ID Example
26
The longer the service has to operate the lower its
availability may be, since for example more maintenance
might be needed. 101 Same for technology readiness
27
If the market size is bigger the system may be less available as it cannot cope with the entire demand for
service. 102 Same for system readiness
28 Same rationale as for 27. 103 Competitor may higher similar companies in the supply chain and affect ist performance.
29
Wrong V&V may result in differences for availability
"influencers" such as maintanance operations between
designed times and actual times (as an example). 104
Elongation or reduction in schedule may limit performance of
supply chain.
30 Same rationale as for 29, but related to the design. 105 Customer involvement may reduce supply chain performance.
31 The longer the service is in operation the higher the risk to be impacted by debris. 106
Export regulations may limit the use of some suppliers and put in danger supply chain.
32
The longer it takes to put the service in operation the
higher the risk of debris impact (because debris increases with time). 107
Export regulations may result in long lead times, influencing schedule.
33
Disposal needs may require maneovres through areas with
different debris content than expected during the design
phase. 108
Frequency allocation decisions may take long time, influencing
design decisions and thus schedule.
34 Same rationale as for 31, but with radiation. 109 N/A
35 The launch window influences the radiation levels, as they depend on the solar cycle. 110 The higher the obsolescence risk the higher the cost uncertainty.
36
New or different requirements may need going through
areas with different debris content. 111 Same for TRL
37
For example, a change in requirements to have less mass
may result in less protection against radiation. 112 Same for SRL
38 Same rationale as for 31, but with weather hazard. 113
If availability is lower than expected, more maintanance
operations than planned are required and therefore operational costs are increased.
39
New or modified requirements resulting in ground segment
locations with riskier weather hazards. 114 The longer it takes to develop the more expensive it is.
40 Export regulations may result in launching or installing the ground segment in locations with riskier weather hazards. 115
A non-working supply chain will imply additional costs in terms of controlling, overseeing, etc.
41
Early obsolescences once in operation may make the
system not upgradable or supportable and die before expected. 116
If requirements are unstable, cost cannot be determined, thus ist uncertainty is higher.
42
The lower tha actual reliability the higher the risk to
become non-operational before planned end of life. Same
in opposite directin. 117
The more the customer is involved the bigger the team in order to
accommodate analysis requests, progress reports, travel, etc.
43
The higher the debris content the higher the impact risk,
the higher the lifetime uncertainty. 118
Export regulations may imply over-purchasing, legal agents,
border taxes, etc.
44
The higher the radiation levels the less time the system can
perform in orbit with the required performance. Note that radiation affects lifetime, not performance itself. 119
Technical capability is determined by the key people of the project mainly.
45
Only applicable if there is only one ground segment in one
location and it is really work intensive for the system. 120
Technical capability is measured against the requirements to be
fulfilled.
46
If the system service beyond ist planned capacity, it may ware out its components faster and therefore reduce the
lifetime of the system. 121
Technical capbilitity is limited or augmented by the level and type
of customer involvement.
47 Same rationale as for 46. 122 Technical capability may be limited by the use of export-regulated technologies, knowledge, etc.
48
Wrong assumptions for the verification of the system
lifetime may result in a different actual lifetime. 123 Competitor may hire key people.
49 Same rationale as for 48, but with design. 124
The longer it takes the more difficult retaining key people is. For
senior people the higher the risk of severe illness or death.
50 N/A 125 Key people may be incompatible with counter-parts at customer.
American Institute of Aeronautics and Astronautics
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Table 12 cont. Justification for uncertainty interdependencies.
ID Example ID Example
51
Unplanned disposal maneovres may require fuel that is
taken out of the planned system lifetime. 126
Export regulations may limit the use of key people depending on
their nationality, etc.
52
Performance of the system may be degraded if more users have to be served. Even if availability is ok, parameters
such as latency might be affected.. 127
The capability of thhe supply chain may influence decisions that
jeopardize the validity of the VV program.,
53 Same rationale as for 52. 128 The VV validity< depends on the technical capability of the company designing the VV strategy.
54
The performance of the system depends on the technical
capabiltiy of the manufacturer. 129 The suitability of the design enables good VV.
55 Export regulations may limit the service from operating in various regions, limiting therefore the market size. 130 The stability and adequacy of the requirements enable proper VV.
56
If the verification is uncertain, the actual performance of
the system is also uncertain. 131
Customer involvement may jeopardize validity of the VV
program.
57 Same rationale as for 56, but with design. 132 Technical capability of the manufacturer enables good design.
58 Performance is measured against requirements. 133
The stability and adequacy of the requirements enable proper
desighn
59 N/A 134
Customer involvement may jeopardize validity of the design
program.
60
If the system is very good it may bring attraction from
more users than initially expected (the ones that were outside the market target) 135
New estimations on the market size may result in adaptation of requirements.
61
Competitors may support the increase of the market size
with the same rationale as for 60, or giving perhaps more presence. 136
Introduction of competitors in the market may result in adaptation of requirements to be more competitive.
62
If the system is late and is put into service once the market
is established, then the market size uncertainty wouild be lower. 137
The longer it takes to develop, the more probable stakeholders may change requirements.
63
The availability performance of the system may influence
competitor decisions to go into the same market. 138 N/A
64 Same rationale as for 63, but with performance. 139 Variation in cost may lead to modify (upgrade or waive) requirements.
65
The certainty of a market size may influence the
competitors to go into that market. 140
Technical capability of the manufacturer may lead to modify
(upgrade or waive) requirements.
66
If the market capture of the system is too big or too low
may influence the competiro to go or not to go into the
same market. 141
The more the customer is involved the more probable
requirements will evolve.
67
If time to market is too high, competitors may want to try
to advance into the market (fort example Microsoft over Apple). 142 Export regulations may result in updating requirements.
68 Availability influences customer satisfaction. 143 Same as for 142.
69 Performance influences customer satisfaction. 144 Same as for 142.
70 Competitors influence customer decision. 145 Same as for 143.
71 Time to market influence how many customers the competitor takes before the system is put into service. 146
Problems with schedule may drive the customer to become more involved, and the opposite.
72
Time to develop the system depends on availability of
technology. 147
Problems with the supply chain may drive the customer to
become more inolved, and the opposite.
73 Same as for 72, but with system readiness. 148 Problems with the technical capabiltiy may drive the customer to become more involved, and the opposite.
74
Performance of the supply chain influence the time to
market. 149
Problems with the cost may drive the customer to become more
involved, and the opposite.
75
Technical capability of the manufacturer influence how
long they need to develop the system. 150
Technical capability depends on the capabilities of the supply
chain.
American Institute of Aeronautics and Astronautics
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Acknowledgments
The present research has been developed under the DARPA/NASA Ames Contract Number: NNA11AB35C on
the Fractionated Space Systems F6 project awarded to the Stevens Institute of Technology. The authors would like
to thank Dr. Owen Brown for his ideas and feedback on the elements presented in this paper.
References 1Weigel, A. L., and Hastings, D. E., “Measuring the Value of Designing for Uncertain Future Downward Budget
Instabilities,” Journal of Spacecraft and Rockets, Vol. 41, No. 1, 2004, pp. 111-119. 2deNeufville, R, “Uncertainty Management for Engineering Systems Planning and Design,” Engineering Systems
Symposium, MIT, Cambridge, MA, 2004. 3Walton, M. A, and Hastings, D. E., “Applications of Uncertainty Analysis Applied to Architecture Selection of Satellite
Systems” Journal of Spacecraft and Rockets, Vol. 41, No. 1, 2004, pp. 75-84. 4Walton, Myles, A., “Managing Uncertainty in Space Systems Conceptual Design Using Portfolio Theory,” Doctoral Thesis
in Aeronautics and Astronautics, June 2002, Chapter 3. 5Saleh, Joseph H., Hastings, D. E., and Newman, D. J., “Flexibility in System Design and Implications for Aerospace
Systems,” Acta Astronautica, Vol. 53, 2003, pp. 927944. 6Saleh, J.H., Marais, K. S., Hastings, D. E., and Newman, D. J., “The Case for Flexibility in System Design,” INCOSE 2002
International Symposium, July-August 2002, Las Vegas, NV. 7Hastings, D. E., Weigel, A. L., and Walton, M. A., “Incorporating uncertainty into conceptual design of space system
architectures,” Massachusetts Institute of Technology, Engineering Systems Division, Working Paper Series, ESD-WP-2003-
01.01-ESD Internal Symposium. 8Fuchs, M., Neumaier, A., and Girimonte, D., “Uncertainty modeling in autonomous robust spacecraft system design,”
PAMM Proc. Appl. Math. Mech. 7, 2060042 (2007) / DOI 10.1002/pamm.200700450. 9Thunnissen, D. P., and Tsuyuki, G. T., “Margin determination in the design and development of a thermal control system,”
SAE Transactions, Vol. 113, No. 1, 2004, pp. 899-916. 10McManus, H., and Hastings, D., "A framework for understanding uncertainty and its mitigation and exploitation in complex
systems," Engineering Management Review, IEEE , vol.34, no.3, pp.81, Third Quarter 2006. 11Yao, W., Guo, J., Chen, X., and Tooren, M.v., “Utilizing Uncertainty Multidisciplinary Design Optimization for
Conceptual Design of Space Systems,” 8th Conference on Systems Engineering Research, March 17-19, 2010, Hoboken, NJ. 12Guo, J., Yao, W., and Gill, E. K. A., “Uncertainty multidisciplinary design optimization of space systems in the presence of
new attributes,” 61st International Astronautical Congress, Prague, CZ. 13Guo, J., Yao, W., and Gill, E., “Uncertainty multidisciplinary design optimization of distributed space systems,” 6th
International Workshop on Satellite Constellation and Formation Flying, Taipei, Taiwan, November 1-3, 2010.
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